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Applied Optics

Applied Optics


  • Editor: Joseph N. Mait
  • Vol. 52, Iss. 6 — Feb. 20, 2013
  • pp: 1173–1182

Flexible near-infrared diffuse optical tomography with varied weighting functions of edge-preserving regularization

Liang-Yu Chen, Min-Cheng Pan, and Min-Chun Pan  »View Author Affiliations

Applied Optics, Vol. 52, Issue 6, pp. 1173-1182 (2013)

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In this paper, a flexible edge-preserving regularization algorithm based on the finite element method is proposed to reconstruct the optical-property images of near-infrared diffuse optical tomography. This regularization algorithm can easily incorporate with varied weighting functions, such as a generalized Lorentzian function, an exponential function, or a generalized total variation function. To evaluate the performance, results obtained from Tikhonov or edge-preserving regularization are compared with each other. As found, the edge-preserving regularization with the generalized Lorentzian function is more attractive than that with other functions for the estimation of absorption-coefficient images concerning functional tomographic images to discover functional information of tested phantoms/tissues.

© 2013 Optical Society of America

OCIS Codes
(100.3190) Image processing : Inverse problems
(170.3010) Medical optics and biotechnology : Image reconstruction techniques
(170.6960) Medical optics and biotechnology : Tomography

ToC Category:
Medical Optics and Biotechnology

Original Manuscript: September 11, 2012
Revised Manuscript: December 17, 2012
Manuscript Accepted: January 9, 2013
Published: February 13, 2013

Virtual Issues
Vol. 8, Iss. 3 Virtual Journal for Biomedical Optics

Liang-Yu Chen, Min-Cheng Pan, and Min-Chun Pan, "Flexible near-infrared diffuse optical tomography with varied weighting functions of edge-preserving regularization," Appl. Opt. 52, 1173-1182 (2013)

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  1. P. J. Cassidy and G. K. Radda, “Molecular imaging perspectives,” J. R. Soc. Interface 2, 133–144 (2005). [CrossRef]
  2. S. R. Arridge and J. C. Schotland, “Optical tomography: forward and inverse problems,” Inverse Probl. 25, 123010 (2009). [CrossRef]
  3. P. Charbonnier, L. Blanc-Feraud, G. Aubert, and M. Barlaud, “Deterministic edge-preserving regularization in computed imaging,” IEEE Trans. Image Process. 6, 298–311 (1997). [CrossRef]
  4. V. B. S. Prasath and A. Singh, “A hybrid convex variational model for image restoration,” Appl. Math. Comput. 215, 3655–3664 (2010).
  5. M. Rivera and J. L. Marroquin, “Adaptive rest condition potentials: first and second order edge-preserving regularization,” Comput. Vis. Image Underst. 88, 76–93 (2002). [CrossRef]
  6. D. Lazzaro and L. B. Montefusco, “Edge-preserving wavelet thresholding for image denoising,” J. Comput. Appl. Math. 210, 222–231 (2007). [CrossRef]
  7. A. H. Delaney and Y. Bresler, “Globally convergent edge-preserving regularized reconstruction: an application to limited-angle tomography,” IEEE Trans. Image Process. 7, 204–221 (1998). [CrossRef]
  8. R. Pan and S. J. Reeves, “Efficient Huber-Markov edge-preserving image restoration,” IEEE Trans. Image Process. 15, 3728–3735 (2006). [CrossRef]
  9. H. Zhang, Z. Shang, and C. Yang, “A non-linear regularized constrained impedance inversion,” Geophys. Prospect. 55, 819–833 (2007). [CrossRef]
  10. H. Zhang, Z. Shang, and C. Yang, “Adaptive reconstruction method of impedance model with absolute and relative constraints,” J. Appl. Geophys. 67, 114–124 (2009). [CrossRef]
  11. G. Vicidomini, P. Boccacci, A. Diaspro, and M. Bertero, “Application of the split-gradient method to 3D image deconvolution in fluorescence microscopy,” J. Microsc. 234, 47–61 (2009). [CrossRef]
  12. X. Gu and L. Gao, “A new method for parameter estimation of edge-preserving regularization in image restoration,” J. Comput. Appl. Math. 225, 478–486 (2009). [CrossRef]
  13. R. Zanella, P. Boccacci, L. Zanni, and M. Bertero, “Efficient gradient projection methods for edge-preserving removal of Poisson noise,” Inverse Probl. 25, 045010 (2009). [CrossRef]
  14. A. Jalobeanu, L. Blance-Feraud, and J. Zerubia, “Hyperparameter estimation for satellite image restoration using a MCMC maximum-likelihood method,” Pattern Recogn. 35, 341–352 (2002). [CrossRef]
  15. P. Lobel, L. Blanc-Feraud, Ch. Pichot, and M. Barlaud, “A new regularization scheme for inverse scattering,” Inverse Probl. 13, 403–410 (1997). [CrossRef]
  16. J. M. Bardsley, and J. Goldes, “An iterative method for edge-preserving MAP estimation when data-noise is Poisson,” SIAM J. Sci. Comput. 32, 171–185 (2010). [CrossRef]
  17. B. Omrane, Y. Goussard, and J. Laurin, “Constrained inverse near-field scattering using high resolution wire grid models,” IEEE Trans. Antennas Propag. 59, 3710–3718 (2011). [CrossRef]
  18. N. Villain, Y. Goussard, J. Idier, and M. Allain, “Three-dimensional edge-preserving image enhancement for computed tomography,” IEEE Trans. Med. Imag. 22, 1275–1287 (2003). [CrossRef]
  19. D. F. Yu, and J. A. Fessler, “Three-dimensional non-local edge-preserving regularization for PET transmission reconstruction,” in Nuclear Science Symposium Conference Record (IEEE, 2000), pp. 1566–1570.
  20. C. Samson, L. Blanc-Feraud, G. Aubert, and J. Zerubia, “A variational model for image classification and restoration,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 460–472 (2000). [CrossRef]
  21. R. Casanova, A. Silva, and A. R. Borges, “MIT image reconstruction based on edge-preserving regularization,” Physiol. Meas. 25, 195–207 (2004). [CrossRef]
  22. L. Blanc-Feraud, and M. Barlaud, “Edge preserving restoration of astrophysical images,” Vistas Astron. 40, 531–538 (1996). [CrossRef]
  23. L. Y. Chen, M.-C. Pan, and M.-C. Pan, “Implementation of edge-preserving regularization for frequency-domain diffuse optical tomography,” Appl. Opt. 51, 43–54 (2012). [CrossRef]
  24. S. Teboul, L. Blanc-Feraud, G. Aubert, and M. Barlaud, “Variational approach for edge-preserving regularization using coupled PDE’s,” IEEE Trans. Image Process. 7, 387–397 (1998). [CrossRef]
  25. M.-C. Pan, C. H. Chen, L. Y. Chen, M.-C. Pan, and Y. M. Shyr, “Highly resolved diffuse optical tomography: a systematic approach using high-pass filtering for value-preserved images,” J. Biomed. Opt. 13, 024022 (2008). [CrossRef]

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